This invention relates to a system for predicting impact on a vehicle by using neural networks. In particular, the present invention relates to a method for predicting impact and an impact prediction system for realizing the same by using the neural networks that are previously trained with crash pulses to determine, according to the learning results, if a crash is severe enough to require the deployment of a passive restraint system such as an air bag system for protecting an occupant in a vehicle.
Various techniques have recently been developed to provide the security of an occupant in a vehicle when it crashes. An air bag system is one of such restraint systems to ensure passive safety and is equipped in most automobiles found in a marketplace. A typical air bag system comprises a bag-like cushion, an inflator connected to the cushion, a crash sensor or an air bag sensor, and a determination circuit. When an automobile collides with something the crash sensor detects the impact to supply a detection signal to the determination circuit associated therewith. The determination circuit determines if a crash is severe enough to trigger the inflator. If necessary, the gas-forming agent is burned in the inflator to generate inert gas such as gaseous nitrogen with which the cushion will inflate immediately after the collision. When an occupant is forced into the cushion already deployed, the gas within the air bag flows out of the cushion to absorb impact on the human body. Thus the air bag system will operate such that, in case of emergency, the cushion inflates instantly in response to the pressure of a reaction gas discharged from the inflator, thereby protecting the occupant.
In most cases, the above mentioned crash sensor is a velocity change sensor. The velocity change sensor generally comprises a mass supported by the elastic member that bounces back and force when the entire structure is accelerated or decelerated. When the mass bounces beyond a predetermined amount the velocity change sensor indicates detection of the crash. Such velocity change sensor mounted on a vehicle is required to have sufficiently short response periods relative to the relatively long period of the decelerated crash velocity.
On sudden collision, the curve of acceleration such as a crash pulse has an extremely complex shape and duration thereof is not constant, so that it is difficult to grasp the entire shape and duration of the crash pulse. FIG. 1 shows experimental curves of acceleration, velocity and displacement. As well known in the art, the velocity is obtained by means of integrating the acceleration and the displacement is obtained by means of integrating the velocity. The characteristic curves shown in FIG. 1(a) are obtained when an automobile collides with a pole at 14 miles per hour (MPH) or 22.5 kilometers per hour while FIG. 1(b) shows those obtained on crash at relatively low speed of 8 MPH (=12.8 km/h). In FIGS. 1(a) and 1(b), .alpha., v and d represent characteristic curves for acceleration, velocity and displacement, respectively. As apparent from these figures, the acceleration curve has a complex time-sequential amplitude characteristic. Many approaches have thus been proposed to improve accuracy of collected data or to improve response characteristic for a sensor, thereby reducing the time duration up to detecting the impact.
Newer electronic technologies have developed for this purpose and consideration can now be given to electronic sensors.
Highly sensitized electronic sensors are inevitably complicated in structure as compared with conventional crash sensors. For example, damping of a sensor mass of a conventional sensor depends on the viscous drag or the moment of inertia, which can not be used in the recent electronic sensors. In addition, too much sensitized electronic sensors are prone to deploying the air bag when a restraint is not required.
To solve this problem of incorrect deployment, the determination circuit has been improved in various ways. For example, a plurality of sensors are arranged in parallel and each of them supplies an output signal to a determination circuit. The determination circuit carries out AND operation in response to these output signals. Alternative determination circuit is operable based on a prediction algorithm to calculate prediction parameters according to previously measured values for a plurality of crash pulses. The crash pulses are obtained by tests in practice such as the frontal and rear vehicle collision test.
An integral prediction algorithm may be used as the algorithm of the type described. Sensed data representing the acceleration curve at an initial period of deceleration is supplied to a low-pass filter to remove noise components. The filtered result is integrated in a predetermined manner. This prediction algorithm takes an advantage of the principle that the filtered acceleration curve can be approximated into line segments at a certain gradient. A value of the gradient is used as a parameter to determine whether impact is going to happen.
The air bag system in current application is designed in cooperation with a seat belt device to protect an occupant from being forced into a windshield or a steering wheel. As mentioned above, the cushion of the air bag system is required to be completely deployed during impact just in front of the occupant before the occupant advances too much.
With respect to a proper balance of a displacement of the occupant and a starting time necessary to initiate deployment of the cushion. The Time to Fire (TTF) is advocated as the nominal reference value for this starting time to trigger the inflator. The TTF is described now concerning FIG. 2. It is assumed that the starting time interval required for completely deploying the air bag B is 30 milliseconds, which is determined on the basis of experiences and previous experiments in practice. In this event, the time instance at which the air bag B starts to deploy coincides with that when the inflator is triggered. T(TTF) can be defined as the time interval from the moment of collision to TTF. Accordingly the T(TTF) can be given by the following equation: EQU T(TTF)=T5"-30 (ms.) (1),
where T5" represents the time interval from the moment of collision to a moment where the head of the occupant is moved five inches ahead from the normal position by crashing force.
Thus the crash sensor must trigger the air bag system before passing this TTF to completely deploy the cushion and provide the proper degree of protection for the occupant.
To calculate the TTF, a value of the accelerated or decelerated velocity is used as a reference parameter for a conventional prediction algorithm. The sensed data supplied from the velocity change sensor is, however, the time sequential data having a non-periodical irregular wave-form with many peaks and valleys. The auto-correlation of such wave-form only results in a featureless pattern. When using the wave-form of the sensed data indicative of change of velocity as a wave-form pattern for analysis an extremely complex algorithm of feature extraction is required to recognize such a wave-form pattern. This means that a conventional program designated for this purpose is capable of extracting the data pattern of the type described only with a complex algorithm designed therefor. The program is, if being completed, less flexible and generalizable to a specification change such as an addition of program function or the like.
In addition, conventional prediction algorithms have no quantitative function to predict change of the value of acceleration or other physical amounts with time after collision. Accordingly it is impossible with this algorithm to previously obtain the actual time instance of the period of T5" after collision. The determination circuit implementing the above mentioned prediction algorithm may determine whether it is necessary to deploy the air bag only according to the result of analogical estimation by means of merely comparing the actual crash pulses with reference crash pulses.